Business and Human Rights Dispute Settlement: The OECD NCPs as Grievance Mechanism
Bibliographic record
Abstract
Abstract This chapter presents a qualitative and quantitative study of 225 specific instances concluded by OECD National Contact Points (NCPs) between 2001 and 2022. The grievances in these cases were accepted and the relevant NCPs offered to the parties one or more of the following dispute resolution services: good office, mediation, and conciliation. The study first provides aggregated statistical data on variables such as the processes used by NCPs in these cases, the participation of the parties, and the outcome. The study then delves more deeply into some of these statistics, for instance examining which NCPs saw the most agreements reached and which mechanisms were offered by each NCP. Logistic regression analysis follows, to see if any meaningful cross-observation relationships can be found. In addition, the study undertakes a comparative qualitative analysis of the dispute resolution mechanisms used by different NCPs and the manner in which they are used. The goals of this study are to uncover trends as well as inconsistencies in the dispute resolution practices of different NCPs and to identify those mechanisms, or combinations of mechanisms, that have proven effective in the resolution of grievances.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".